Revert "IVGCVSW-6267 Add support of Unidirectional Sequence Lstm fp32/fp16 to Cl"

This reverts commit ad9171701e6032b3ddf3573f85780bae30c512c6.

Reason for revert: cannot update ACL pin until 22.02 release.

!ComputeLibrary:7150

Change-Id: Ic19a3c2fe5d6f7e5568174f18ea73684b269f72d

12 files changed, 166 insertions, 1253 deletions

diff --git a/docs/02_operator_list.dox b/docs/02_operator_list.dox
index ee0341ca2b..b51ba5775e 100644
--- a/docs/02_operator_list.dox
+++ b/docs/02_operator_list.dox
@@ -3337,20 +3337,6 @@ where N = batches, C = channels, H = height, W = width
          <tr><th>Weight Types
          <tr><td>FLOAT32
       </table>
-   <td>GpuAcc
-    <td>
-        <ul>
-         <li>All
-        </ul>
-    <td>
-     <table>
-        <tr><th>Input Types
-        <tr><td>FLOAT32
-     </table>
-     <table>
-        <tr><th>Weight Types
-        <tr><td>FLOAT32
-     </table>
 <tr>
   <td rowspan="3">UnmapLayer
   <td rowspan="3" style="width:200px;"> Layer to perform unmap operation on tensor.
diff --git a/src/backends/cl/ClLayerSupport.cpp b/src/backends/cl/ClLayerSupport.cpp
index e52f578bc0..e5204e4d5b 100644
--- a/src/backends/cl/ClLayerSupport.cpp
+++ b/src/backends/cl/ClLayerSupport.cpp
@@ -78,7 +78,6 @@
 #include "workloads/ClSubtractionWorkload.hpp"
 #include "workloads/ClTransposeConvolution2dWorkload.hpp"
 #include "workloads/ClTransposeWorkload.hpp"
-#include "workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp"
 #endif
 
 
@@ -213,13 +212,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                              infos[1],
                                              *(PolymorphicDowncast<const BatchToSpaceNdDescriptor*>(&descriptor)),
                                              reasonIfUnsupported);
-        case LayerType::Cast:
-            return IsCastSupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::ChannelShuffle:
-            return IsChannelShuffleSupported(infos[0],
-                                             infos[1],
-                                             *(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)),
-                                             reasonIfUnsupported);
         case LayerType::Comparison:
             return IsComparisonSupported(infos[0],
                                          infos[1],
@@ -244,14 +236,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
             return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported);
         case LayerType::ConvertFp32ToFp16:
             return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::ConvertBf16ToFp32:
-            return LayerSupportBase::IsConvertBf16ToFp32Supported(infos[0],
-                                                                  infos[1],
-                                                                  reasonIfUnsupported);
-        case LayerType::ConvertFp32ToBf16:
-            return LayerSupportBase::IsConvertFp32ToBf16Supported(infos[0],
-                                                                  infos[1],
-                                                                  reasonIfUnsupported);
         case LayerType::Convolution2d:
         {
             if (infos.size() != 4)
@@ -280,34 +264,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                                 reasonIfUnsupported);
             }
         }
-        case LayerType::Convolution3d:
-        {
-            if (infos.size() != 4)
-            {
-                throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. "
-                                               "TensorInfos should be of format: {input, output, weights, biases}.");
-            }
-
-            auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor));
-            if (infos[3] == TensorInfo())
-            {
-                return IsConvolution3dSupported(infos[0],
-                                                infos[1],
-                                                desc,
-                                                infos[2],
-                                                EmptyOptional(),
-                                                reasonIfUnsupported);
-            }
-            else
-            {
-                return IsConvolution3dSupported(infos[0],
-                                                infos[1],
-                                                desc,
-                                                infos[2],
-                                                infos[3],
-                                                reasonIfUnsupported);
-            }
-        }
         case LayerType::DepthToSpace:
             return IsDepthToSpaceSupported(infos[0],
                                            infos[1],
@@ -405,17 +361,16 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                    *(PolymorphicDowncast<const LstmDescriptor*>(&descriptor)),
                                    lstmParamsInfo.value(),
                                    reasonIfUnsupported);
-        case LayerType::Map:
-            return true;
-        case LayerType::MemCopy:
-            return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::MemImport:
-            return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::Merge:
-            return LayerSupportBase::IsMergeSupported(infos[0],
-                                                      infos[1],
-                                                      infos[2],
-                                                      reasonIfUnsupported);
+        case LayerType::QLstm:
+            return IsQLstmSupported(infos[0],
+                                    infos[1],
+                                    infos[2],
+                                    infos[3],
+                                    infos[4],
+                                    infos[5],
+                                    *(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)),
+                                    lstmParamsInfo.value(),
+                                    reasonIfUnsupported);
         case LayerType::Maximum:
             return IsMaximumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
         case LayerType::Mean:
@@ -451,16 +406,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                         reasonIfUnsupported);
         case LayerType::Prelu:
             return IsPreluSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
-        case LayerType::QLstm:
-            return IsQLstmSupported(infos[0],
-                                    infos[1],
-                                    infos[2],
-                                    infos[3],
-                                    infos[4],
-                                    infos[5],
-                                    *(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)),
-                                    lstmParamsInfo.value(),
-                                    reasonIfUnsupported);
         case LayerType::Quantize:
             return IsQuantizeSupported(infos[0], infos[1], reasonIfUnsupported);
         case LayerType::QuantizedLstm:
@@ -471,13 +416,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                             infos[4],
                                             quantizedLstmParamsInfo.value(),
                                             reasonIfUnsupported);
-        case LayerType::Rank:
-            return true;
-        case LayerType::Reduce:
-            return IsReduceSupported(infos[0],
-                                     infos[1],
-                                     *(PolymorphicDowncast<const ReduceDescriptor*>(&descriptor)),
-                                     reasonIfUnsupported);
         case LayerType::Reshape:
             return IsReshapeSupported(infos[0],
                                       infos[1],
@@ -488,10 +426,11 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                      infos[1],
                                      *(PolymorphicDowncast<const ResizeDescriptor*>(&descriptor)),
                                      reasonIfUnsupported);
-        case LayerType::Shape:
-            return LayerSupportBase::IsShapeSupported(infos[0],
-                                                      infos[1],
-                                                      reasonIfUnsupported);
+        case LayerType::Reduce:
+            return IsReduceSupported(infos[0],
+                                     infos[1],
+                                     *(PolymorphicDowncast<const ReduceDescriptor*>(&descriptor)),
+                                     reasonIfUnsupported);
         case LayerType::Slice:
             return IsSliceSupported(infos[0],
                                     infos[1],
@@ -576,23 +515,72 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type,
                                                          reasonIfUnsupported);
             }
         }
-        case LayerType::UnidirectionalSequenceLstm:
-            return IsUnidirectionalSequenceLstmSupported(infos[0],
-                                                         infos[1],
-                                                         infos[2],
-                                                         infos[3],
-                                                         infos[4],
-                                                         infos[5],
-                                                         *(PolymorphicDowncast<const
-                                                            UnidirectionalSequenceLstmDescriptor*>(&descriptor)),
-                                                         lstmParamsInfo.value(),
-                                                         reasonIfUnsupported);
+        case LayerType::Cast:
+            return IsCastSupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::ChannelShuffle:
+            return IsChannelShuffleSupported(infos[0],
+                                             infos[1],
+                                             *(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)),
+                                             reasonIfUnsupported);
+        case LayerType::Convolution3d:
+        {
+            if (infos.size() != 4)
+            {
+                throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. "
+                                               "TensorInfos should be of format: {input, output, weights, biases}.");
+            }
+
+            auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor));
+            if (infos[3] == TensorInfo())
+            {
+                return IsConvolution3dSupported(infos[0],
+                                                infos[1],
+                                                desc,
+                                                infos[2],
+                                                EmptyOptional(),
+                                                reasonIfUnsupported);
+            }
+            else
+            {
+                return IsConvolution3dSupported(infos[0],
+                                                infos[1],
+                                                desc,
+                                                infos[2],
+                                                infos[3],
+                                                reasonIfUnsupported);
+            }
+        }
+        case LayerType::MemCopy:
+            return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::MemImport:
+            return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::Map:
+            return true;
         case LayerType::Unmap:
             return true;
+        case LayerType::Merge:
+            return LayerSupportBase::IsMergeSupported(infos[0],
+                                                      infos[1],
+                                                      infos[2],
+                                                      reasonIfUnsupported);
+        case LayerType::Rank:
+            return true;
+        case LayerType::Shape:
+            return LayerSupportBase::IsShapeSupported(infos[0],
+                                                      infos[1],
+                                                      reasonIfUnsupported);
+        case LayerType::ConvertBf16ToFp32:
+            return LayerSupportBase::IsConvertBf16ToFp32Supported(infos[0],
+                                                                  infos[1],
+                                                                  reasonIfUnsupported);
+        case LayerType::ConvertFp32ToBf16:
+            return LayerSupportBase::IsConvertFp32ToBf16Supported(infos[0],
+                                                                  infos[1],
+                                                                  reasonIfUnsupported);
         default:
             // layers not supported in cl by default:
-            // debug, detectionpostprocess, fakequantization,
-            // precompiled, standin, switch, pooling3d
+            // debug, detectionpostprocess, fakequantization, precompiled,
+            // standin, switch, unidirectionalsequencelstm, pooling3d
             return false;
     }
 }
@@ -1427,26 +1415,4 @@ bool ClLayerSupport::IsTransposeSupported(const TensorInfo& input,
     FORWARD_WORKLOAD_VALIDATE_FUNC(ClTransposeWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
 }
 
-bool ClLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
-                                                           const TensorInfo& outputStateIn,
-                                                           const TensorInfo& cellStateIn,
-                                                           const TensorInfo& output,
-                                                           const Optional<TensorInfo>& hiddenStateOutput,
-                                                           const Optional<TensorInfo>& cellStateOutput,
-                                                           const UnidirectionalSequenceLstmDescriptor& descriptor,
-                                                           const LstmInputParamsInfo& paramsInfo,
-                                                           Optional<std::string&> reasonIfUnsupported) const
-{
-    FORWARD_WORKLOAD_VALIDATE_FUNC(ClUnidirectionalSequenceLstmFloatWorkloadValidate,
-                                   reasonIfUnsupported,
-                                   input,
-                                   outputStateIn,
-                                   cellStateIn,
-                                   output,
-                                   hiddenStateOutput,
-                                   cellStateOutput,
-                                   descriptor,
-                                   paramsInfo);
-}
-
 } // namespace armnn
diff --git a/src/backends/cl/ClLayerSupport.hpp b/src/backends/cl/ClLayerSupport.hpp
index 103944e05d..0300fc05c3 100644
--- a/src/backends/cl/ClLayerSupport.hpp
+++ b/src/backends/cl/ClLayerSupport.hpp
@@ -326,16 +326,6 @@ public:
                               const TransposeDescriptor& descriptor,
                               Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
 
-    bool IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
-                                               const TensorInfo& outputStateIn,
-                                               const TensorInfo& cellStateIn,
-                                               const TensorInfo& output,
-                                               const Optional<TensorInfo>& hiddenStateOutput,
-                                               const Optional<TensorInfo>& cellStateOutput,
-                                               const UnidirectionalSequenceLstmDescriptor& descriptor,
-                                               const LstmInputParamsInfo& paramsInfo,
-                                               Optional<std::string&> reasonIfUnsupported) const override;
-
 private:
     const IBackendInternal::IBackendSpecificModelContextPtr m_ModelContextPtr;
 
diff --git a/src/backends/cl/ClWorkloadFactory.cpp b/src/backends/cl/ClWorkloadFactory.cpp
index c561bf2157..0632787db0 100644
--- a/src/backends/cl/ClWorkloadFactory.cpp
+++ b/src/backends/cl/ClWorkloadFactory.cpp
@@ -684,13 +684,6 @@ std::unique_ptr<IWorkload> ClWorkloadFactory::CreateWorkload(LayerType type,
                                                                   m_MemoryManager->GetIntraLayerManager(),
                                                                   m_CLCompileContext);
         }
-        case LayerType::UnidirectionalSequenceLstm :
-        {
-            auto desc = PolymorphicDowncast<const UnidirectionalSequenceLstmQueueDescriptor*>(&descriptor);
-            return MakeWorkloadHelper<ClUnidirectionalSequenceLstmFloatWorkload, NullWorkload>(*desc,
-                                                                                               info,
-                                                                                               m_CLCompileContext);
-        }
         default:
             return nullptr;
     }
diff --git a/src/backends/cl/backend.mk b/src/backends/cl/backend.mk
index fde6b7eeb2..5bef3a786b 100644
--- a/src/backends/cl/backend.mk
+++ b/src/backends/cl/backend.mk
@@ -85,8 +85,7 @@ BACKEND_SOURCES := \
         workloads/ClStridedSliceWorkload.cpp \
         workloads/ClSubtractionWorkload.cpp \
         workloads/ClTransposeConvolution2dWorkload.cpp \
-        workloads/ClTransposeWorkload.cpp \
-        workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
+        workloads/ClTransposeWorkload.cpp
 else
 
 # ARMNN_COMPUTE_CL_ENABLED == 0
diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp
index 1c342149db..7f63e6326c 100644
--- a/src/backends/cl/test/ClLayerTests.cpp
+++ b/src/backends/cl/test/ClLayerTests.cpp
@@ -1025,22 +1025,6 @@ ARMNN_AUTO_TEST_FIXTURE_WITH_THF(QLstm2, ClContextControlFixture, QLstmTest2)
 // QuantizedLstm
 ARMNN_AUTO_TEST_FIXTURE_WITH_THF(QuantizedLstm, ClContextControlFixture, QuantizedLstmTest)
 
-// Unidirectional Sequence Lstm
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatch,
-                              UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatch,
-                              UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32,
-                              UnidirectionalSequenceLstmLayerFloat32Test)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerFloat32TimeMajor,
-                              UnidirectionalSequenceLstmLayerFloat32TimeMajorTest)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjection,
-                              UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNorm,
-                              UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest)
-ARMNN_AUTO_TEST_CASE_WITH_THF(UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjection,
-                              UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest)
-
 // Convert from Float16 to Float32
 ARMNN_AUTO_TEST_FIXTURE_WITH_THF(SimpleConvertFp16ToFp32, ClContextControlFixture, SimpleConvertFp16ToFp32Test)
 // Convert from Float32 to Float16
diff --git a/src/backends/cl/workloads/CMakeLists.txt b/src/backends/cl/workloads/CMakeLists.txt
index 423a4a69d1..6e7dd36f47 100644
--- a/src/backends/cl/workloads/CMakeLists.txt
+++ b/src/backends/cl/workloads/CMakeLists.txt
@@ -125,8 +125,6 @@ list(APPEND armnnClBackendWorkloads_sources
     ClTransposeConvolution2dWorkload.hpp
     ClTransposeWorkload.cpp
     ClTransposeWorkload.hpp
-    ClUnidirectionalSequenceLstmFloatWorkload.cpp
-    ClUnidirectionalSequenceLstmFloatWorkload.hpp
     ClWorkloads.hpp
     ClWorkloadUtils.hpp
 )
diff --git a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
deleted file mode 100644
index cc9aea8486..0000000000
--- a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
+++ /dev/null
@@ -1,903 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "ClUnidirectionalSequenceLstmFloatWorkload.hpp"
-#include "ClWorkloadUtils.hpp"
-
-#include <aclCommon/ArmComputeUtils.hpp>
-#include <aclCommon/ArmComputeTensorUtils.hpp>
-
-#include <armnn/utility/NumericCast.hpp>
-#include <armnnUtils/Permute.hpp>
-#include <cl/test/ClWorkloadFactoryHelper.hpp>
-#include <backendsCommon/WorkloadUtils.hpp>
-
-#include "cl/ClTensorHandle.hpp"
-
-namespace
-{
-unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis)
-{
-    return (numDimensions - axis) - 1;
-}
-} //namespace
-
-namespace armnn
-{
-using namespace armcomputetensorutils;
-
-ClUnidirectionalSequenceLstmFloatWorkload::ClUnidirectionalSequenceLstmFloatWorkload
-    (const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
-     const WorkloadInfo& info,
-     const arm_compute::CLCompileContext& clCompileContext)
-    : FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)
-{
-    // Report Profiling Details
-    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClUnidirectionalSequenceLstmFloatWorkload_Construct",
-                                         descriptor.m_Parameters,
-                                         info,
-                                         GetGuid());
-
-    const arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
-    arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
-
-    TensorInfo inputInfo = info.m_InputTensorInfos[0];
-    TensorInfo outputInfo = info.m_OutputTensorInfos[0];
-
-    arm_compute::DataType armComputeDataType = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetDataType();
-    armnn::DataType armnnDataType = GetArmNNDataType(armComputeDataType);
-
-    TensorShape inputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetShape();
-    TensorShape cellStateLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetShape();
-    TensorShape outputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetShape();
-
-    unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
-    unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
-    unsigned int inputSize = inputLayerShape[2];
-    unsigned int outputSize = outputLayerShape[2];
-    unsigned int numUnits = cellStateLayerShape[1];
-
-    const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
-    const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
-
-    //
-    // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.
-    //
-    if (!m_Data.m_Parameters.m_TimeMajor)
-    {
-        std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute());
-
-        TensorInfo permuteOutInfo = inputInfo;
-        permuteOutInfo.SetShape(timeMajorShapeInput);
-        BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);
-        armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);
-
-        // Permute to time major format.
-        layer->configure(clCompileContext, &input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));
-        m_Permute1.reset(layer.release());
-    }
-
-    //
-    // Split and Concat Tensors
-    //
-    for (unsigned int i = 0; i < maxTime; ++i)
-    {
-        arm_compute::CLTensor splitter_out;
-        arm_compute::CLTensor concat_in;
-
-        auto splitterTensorInfo = inputInfo;
-        auto concatTensorInfo = outputInfo;
-        splitterTensorInfo.SetShape({batchSize, inputSize});
-        concatTensorInfo.SetShape({batchSize, outputSize});
-        BuildArmComputeTensor(splitter_out, splitterTensorInfo);
-        BuildArmComputeTensor(concat_in, concatTensorInfo);
-
-        armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);
-        armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);
-
-        // append to std::vector<arm_compute::CLTensor>
-        m_SplitterOutputsTensors.push_back(std::move(splitter_out));
-        m_ConcatInputsTensors.push_back(std::move(concat_in));
-    }
-
-    for (unsigned int i = 0; i < maxTime; ++i)
-    {
-        // append to std::vector<arm_compute::ICLTensor*>
-        m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]);
-        m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]);
-    }
-
-    //
-    // Split
-    //
-    unsigned int numberDimensions = 3;
-    unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension)
-
-    if (maxTime != 1) // ACL split does not work with only one element to split.
-    {
-        ViewsDescriptor splitterDesc(maxTime, numberDimensions);
-        unsigned int splitterDimSizes[3] = {1, batchSize, inputSize};
-        for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx)
-        {
-            splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);
-            for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx)
-            {
-                splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);
-            }
-        }
-
-        std::set<unsigned int> splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput);
-
-        std::unique_ptr<arm_compute::CLSplit> split_layer(new arm_compute::CLSplit());
-        unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), *splitAxis.begin());
-        if (!m_Data.m_Parameters.m_TimeMajor)
-        {
-            split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);
-        }
-        else
-        {
-            split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit);
-        }
-
-        split_layer->prepare();
-        m_Splitter.reset(split_layer.release());
-    }
-
-    //
-    // Lstm
-    //
-    arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param;
-
-    m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
-
-    m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
-
-    m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
-
-    m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
-
-    m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
-
-    m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
-
-    m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
-
-    m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
-
-    m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
-    BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
-
-    // for future reference: check the AndroidNN API for the logic here
-    if (!m_Data.m_Parameters.m_CifgEnabled)
-    {
-        m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
-
-        m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
-
-        m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        if (m_Data.m_CellToInputWeights != nullptr)
-        {
-            BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
-        }
-
-        m_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
-
-        lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
-                                   m_RecurrentToInputWeightsTensor.get(),
-                                   m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : nullptr,
-                                   m_InputGateBiasTensor.get());
-    }
-
-    if (m_Data.m_Parameters.m_ProjectionEnabled)
-    {
-        m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
-
-        m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>();
-        if (m_Data.m_ProjectionBias != nullptr)
-        {
-            BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
-        }
-
-        lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
-                                         m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : nullptr);
-    }
-
-    if (m_Data.m_Parameters.m_PeepholeEnabled)
-    {
-        m_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
-
-        m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
-
-        lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
-    }
-
-    if (m_Data.m_Parameters.m_LayerNormEnabled)
-    {
-        m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        if (!m_Data.m_Parameters.m_CifgEnabled)
-        {
-            BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());
-        }
-
-        m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());
-
-        m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());
-
-        m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>();
-        BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());
-
-        auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();
-        lstm_param.set_layer_normalization_params(inputNormWeightTensor,
-                                                  m_ForgetLayerNormWeightsTensor.get(),
-                                                  m_CellLayerNormWeightsTensor.get(),
-                                                  m_OutputLayerNormWeightsTensor.get());
-    }
-
-    arm_compute::ICLTensor& output_state_in = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
-    arm_compute::ICLTensor& cell_state_in   = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
-
-    arm_compute::ICLTensor& output_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
-    arm_compute::ICLTensor& cell_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
-
-    m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>();
-    if (m_Data.m_Parameters.m_CifgEnabled)
-    {
-        // scratch_buffer [num_units * 3, batch_size] with CIFG
-        BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType));
-    }
-    else
-    {
-        // scratch_buffer [num_units * 4, batch_size] without CIFG
-        BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType));
-    }
-
-    // Need to be set at negative threshold to be compatible for ACL
-    float cell_threshold       = m_Data.m_Parameters.m_ClippingThresCell;
-    float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
-
-    // For preparing the object for the class ActivationLayerInfo, consider 5 situations
-    arm_compute::ActivationLayerInfo activationLayerInfo =
-        ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
-
-    for (unsigned int i = 0; i != maxTime; ++i)
-    {
-        // Set LSTM input and output ITensors depending on:
-        // input format (timeMajor) & number of LSTM batches (maxTime).
-        arm_compute::ICLTensor* outputLSTM;
-        arm_compute::ICLTensor* inputLSTM;
-        // If there is only one LSTM time major batch, we will not concat OR permute.
-        // Set input of LSTM to be first input ITensor.
-        // Set output of LSTM to be final output ITensor.
-        // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
-        if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor)
-        {
-            TensorShape inputShape = GetTensorShape((&input)->info()->tensor_shape(), 1U);
-            TensorShape outputShape = GetTensorShape((&output)->info()->tensor_shape(), 1U);
-            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
-            TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
-            auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
-            auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
-            (&input)->info()->set_tensor_shape(acl_input_shape_shrink);
-            inputLSTM = const_cast<arm_compute::ICLTensor*>(&input);
-            (&output)->info()->set_tensor_shape(acl_output_shape_shrink);
-            outputLSTM = &output;
-        }
-            // If there is only one LSTM batch major batch, we will not concat, only permute.
-            // Set input of LSTM to be output of initial permute.
-            // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
-            // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
-        else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor)
-        {
-            TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U);
-            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
-            auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
-            m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink);
-            inputLSTM = &m_PermuteFirstOut;
-            outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]);
-        }
-            // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
-        else
-        {
-            inputLSTM = m_SplitterOutputs[i];
-            outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]);
-        }
-
-        std::unique_ptr<arm_compute::CLLSTMLayer> lstm_layer(new arm_compute::CLLSTMLayer());
-        lstm_layer->configure(clCompileContext,
-                              inputLSTM,
-                              m_InputToForgetWeightsTensor.get(),
-                              m_InputToCellWeightsTensor.get(),
-                              m_InputToOutputWeightsTensor.get(),
-                              m_RecurrentToForgetWeightsTensor.get(),
-                              m_RecurrentToCellWeightsTensor.get(),
-                              m_RecurrentToOutputWeightsTensor.get(),
-                              m_ForgetGateBiasTensor.get(),
-                              m_CellBiasTensor.get(),
-                              m_OutputGateBiasTensor.get(),
-                              &output_state_in,
-                              &cell_state_in,
-                              m_ScratchBuffer.get(),
-                              &output_state_out,
-                              &cell_state_out,
-                              outputLSTM,
-                              lstm_param,
-                              activationLayerInfo,
-                              cell_threshold,
-                              projection_threshold);
-
-        m_Layers.emplace_back(std::move(lstm_layer));
-    }
-
-    armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);
-
-    InitializeArmComputeClTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights);
-    InitializeArmComputeClTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights);
-    InitializeArmComputeClTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights);
-    InitializeArmComputeClTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights);
-    InitializeArmComputeClTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights);
-    InitializeArmComputeClTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights);
-    InitializeArmComputeClTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias);
-    InitializeArmComputeClTensorData(*m_CellBiasTensor, m_Data.m_CellBias);
-    InitializeArmComputeClTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias);
-
-    if (!m_Data.m_Parameters.m_CifgEnabled)
-    {
-        InitializeArmComputeClTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights);
-        InitializeArmComputeClTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights);
-        if (m_Data.m_CellToInputWeights != nullptr)
-        {
-            InitializeArmComputeClTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights);
-        }
-        InitializeArmComputeClTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias);
-    }
-
-    if (m_Data.m_Parameters.m_ProjectionEnabled)
-    {
-        InitializeArmComputeClTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights);
-        if (m_Data.m_ProjectionBias != nullptr)
-        {
-            InitializeArmComputeClTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias);
-        }
-    }
-
-    if (m_Data.m_Parameters.m_PeepholeEnabled)
-    {
-        InitializeArmComputeClTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights);
-        InitializeArmComputeClTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights);
-    }
-
-    if (m_Data.m_Parameters.m_LayerNormEnabled)
-    {
-        if (!m_Data.m_Parameters.m_CifgEnabled)
-        {
-            InitializeArmComputeClTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);
-        }
-        InitializeArmComputeClTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);
-        InitializeArmComputeClTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);
-        InitializeArmComputeClTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);
-    }
-
-    // Force Compute Library to perform the necessary copying and reshaping.
-    // After which delete all the input tensors that will no longer be needed.
-    for (uint32_t i = 0; i < m_Layers.size(); ++i)
-    {
-        m_Layers[i]->prepare();
-    }
-
-    //
-    // Concat
-    //
-
-    // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
-    TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U);
-    TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
-    TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
-
-    if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
-    {
-        for (unsigned int i = 0; i < maxTime; ++i)
-        {
-            m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
-        }
-
-        ConcatDescriptor  concatDescriptor(maxTime, numberDimensions);  // maxTime = num inputs (aka. number of views).
-        for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx)
-        {
-            concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx);
-            concatDescriptor.SetConcatAxis(dimension);
-        }
-
-        m_Concat.reset(new arm_compute::CLConcatenateLayer());
-        unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(),
-                                                 concatDescriptor.GetConcatAxis());
-        if (!m_Data.m_Parameters.m_TimeMajor)
-        {
-            TensorInfo concatOuputTensorInfo = outputInfo;
-            concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
-            BuildArmComputeTensor(concat_out, concatOuputTensorInfo);
-            armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);
-
-            m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);
-        }
-        else
-        {
-            m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat);
-        }
-
-        m_Concat->prepare();
-    }
-    // If only one LSTM batch, we do not concat and/or permute.
-    // Must ensure final output info is expanded to correct batch major dimensions.
-    else
-    {
-        if (!m_Data.m_Parameters.m_TimeMajor)
-        {
-            (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));
-        }
-        else
-        {
-            (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
-        }
-    }
-
-    //
-    // Permute: only done if input/output are in batch major format.
-    //
-    if (!m_Data.m_Parameters.m_TimeMajor)
-    {
-        // Output now time major. Permute output back to batch major.
-        std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute());
-        if (maxTime != 1)
-        {
-            layer->configure(clCompileContext, &concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U));
-        }
-        else
-        {
-            layer->configure(clCompileContext, m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U));
-        }
-        m_Permute2.reset(layer.release());
-    }
-
-    FreeUnusedTensors();
-}
-
-void ClUnidirectionalSequenceLstmFloatWorkload::Execute() const
-{
-    ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClUnidirectionalSequenceLstmFloatWorkload_Execute", GetGuid());
-    if (m_Permute1)
-    {
-        m_Permute1->run();
-    }
-    if (m_Splitter)
-    {
-        m_Splitter->run();
-    }
-    for (uint32_t i = 0; i < m_Layers.size(); ++i)
-    {
-        m_Layers[i]->run();
-    }
-    if (m_Concat)
-    {
-        m_Concat->run();
-    }
-    if (m_Permute2)
-    {
-        m_Permute2->run();
-    }
-}
-
-arm_compute::Status
-ClUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input,
-                                                  const TensorInfo& outputStateIn,
-                                                  const TensorInfo& cellStateIn,
-                                                  const TensorInfo& output,
-                                                  const Optional<TensorInfo>& hiddenStateOutput,
-                                                  const Optional<TensorInfo>& cellStateOutput,
-                                                  const UnidirectionalSequenceLstmDescriptor& descriptor,
-                                                  const LstmInputParamsInfo& paramsInfo)
-{
-    IgnoreUnused(hiddenStateOutput, cellStateOutput);
-
-    TensorShape inputLayerShape  = input.GetShape();
-    TensorShape outputLayerShape = outputStateIn.GetShape();
-
-    unsigned int maxTime    = descriptor.m_TimeMajor?inputLayerShape[0]:inputLayerShape[1];
-    unsigned int batchSize  = descriptor.m_TimeMajor?inputLayerShape[1]:inputLayerShape[0];
-    unsigned int inputSize  = inputLayerShape[2];
-    unsigned int outputSize = outputLayerShape[2];
-
-    const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
-    const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
-
-    arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK,
-                                                             "Permute1 status");
-    arm_compute::Status statusSplit    = arm_compute::Status(arm_compute::ErrorCode::OK,
-                                                             "Split status");
-    arm_compute::Status statusLSTM     = arm_compute::Status(arm_compute::ErrorCode::OK,
-                                                             "LSTM status");
-    arm_compute::Status statusConcat   = arm_compute::Status(arm_compute::ErrorCode::OK,
-                                                             "Concat status");
-    arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK,
-                                                             "Permute2 status");
-
-    const arm_compute::TensorInfo aclInputInfo  = armcomputetensorutils::BuildArmComputeTensorInfo(input);
-    const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
-
-    //
-    // Permute validate
-    //
-    TensorInfo              permuteOutInfo    = TensorInfo(input);
-    arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo);
-    if (!descriptor.m_TimeMajor)
-    {
-        statusPermute1 = arm_compute::CLPermute::validate(&aclInputInfo,
-                                                          &aclPermuteOutInfo,
-                                                          arm_compute::PermutationVector(0U, 2U, 1U));
-    }
-
-    //
-    // Split and Concat Tensors validate
-    //
-    std::vector<arm_compute::TensorInfo>         splitterOutputsTensorInfos;
-    std::vector<arm_compute::TensorInfo>         concatInputsTensorInfos;
-    std::vector<arm_compute::ITensorInfo*>       splitterOutputsTensorInfosPtr;
-    std::vector<const arm_compute::ITensorInfo*> concatInputsTensorInfosPtr;
-    splitterOutputsTensorInfos.reserve(maxTime);
-    concatInputsTensorInfos.reserve(maxTime);
-    for (unsigned int i = 0; i < maxTime; ++i)
-    {
-        arm_compute::TensorInfo splitter_out;
-        arm_compute::TensorInfo concat_in;
-
-        auto splitterTensorInfo = TensorInfo(input);
-        auto concatTensorInfo   = TensorInfo(output);
-        splitterTensorInfo.SetShape({batchSize, inputSize});
-        concatTensorInfo.SetShape({batchSize, outputSize});
-
-        arm_compute::TensorInfo aclSplitterTensorInfo
-                                    = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo);
-        arm_compute::TensorInfo aclConcatTensorInfo
-                                    = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo);
-
-        splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo);
-        concatInputsTensorInfos.emplace_back(aclConcatTensorInfo);
-        splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]);
-        concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]);
-    }
-
-    //
-    // Split validate
-    //
-    unsigned int numberDimensions = 3;
-    unsigned int dimension        = 0; // splitting on 0-dimension (i.e. maxTime dimension)
-    unsigned int aclAxisSplit     = CalcAclAxis(numberDimensions, dimension);
-
-    if (maxTime != 1) // ACL split does not work with only one element to split.
-    {
-        if (!descriptor.m_TimeMajor)
-        {
-            statusSplit = arm_compute::CLSplit::validate(&aclPermuteOutInfo,
-                                                         splitterOutputsTensorInfosPtr,
-                                                         aclAxisSplit);
-        }
-        else
-        {
-            statusSplit = arm_compute::CLSplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit);
-        }
-    }
-
-    //
-    // LSTM validate
-    //
-
-    arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
-
-    const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
-    const TensorInfo& outputStateOut = TensorInfo(outputStateIn.GetShape(), input.GetDataType());
-    const TensorInfo& cellStateOut = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
-
-    // The inputs and outputs
-    const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
-    const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);
-    const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);
-    const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);
-    const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);
-
-    // Basic parameters
-    const arm_compute::TensorInfo aclInputToForgetWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights());
-    const arm_compute::TensorInfo aclInputToCellWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights());
-    const arm_compute::TensorInfo aclInputToOutputWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights());
-    const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights());
-    const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights());
-    const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights());
-    const arm_compute::TensorInfo aclForgetGateBiasInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias());
-    const arm_compute::TensorInfo aclCellBiasInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetCellBias());
-    const arm_compute::TensorInfo aclOutputGateBiasInfo
-                                      = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias());
-
-    arm_compute::TensorInfo aclInputToInputWeightsInfo;
-    arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;
-    arm_compute::TensorInfo aclCellToInputWeightsInfo;
-    arm_compute::TensorInfo aclInputGateBiasInfo;
-    arm_compute::TensorInfo aclProjectionWeightsInfo;
-    arm_compute::TensorInfo aclProjectionBiasInfo;
-    arm_compute::TensorInfo aclCellToForgetWeightsInfo;
-    arm_compute::TensorInfo aclCellToOutputWeightsInfo;
-
-    arm_compute::TensorInfo aclInputLayerNormWeightsInfo;
-    arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;
-    arm_compute::TensorInfo aclCellLayerNormWeightsInfo;
-    arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;
-
-
-    if (!descriptor.m_CifgEnabled)
-    {
-        if (descriptor.m_PeepholeEnabled)
-        {
-            aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights());
-        }
-        aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights());
-        aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights());
-        aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias());
-
-        lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo,
-                                         &aclRecurrentToInputWeightsInfo,
-                                         descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr,
-                                         &aclInputGateBiasInfo);
-    }
-
-    if (descriptor.m_ProjectionEnabled)
-    {
-        if (paramsInfo.m_ProjectionBias != nullptr)
-        {
-            aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias());
-        }
-        aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights());
-
-        lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,
-                                               paramsInfo.m_ProjectionBias ? &aclProjectionBiasInfo : nullptr);
-    }
-
-    if (descriptor.m_PeepholeEnabled)
-    {
-        aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights());
-        aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights());
-
-        lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);
-    }
-
-    if (descriptor.m_LayerNormEnabled)
-    {
-        if (!descriptor.m_CifgEnabled)
-        {
-            aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights());
-        }
-        aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights());
-        aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights());
-        aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights());
-
-        lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr :
-                                                        &aclInputLayerNormWeightsInfo,
-                                                        &aclForgetLayerNormWeightsInfo,
-                                                        &aclCellLayerNormWeightsInfo,
-                                                        &aclOutputLayerNormWeightsInfo);
-    }
-
-    // Need to be set at negative threshold to be compatible for ACL
-    float cell_threshold = descriptor.m_ClippingThresCell;
-    float projection_threshold = descriptor.m_ClippingThresProj;
-
-    arm_compute::ActivationLayerInfo activationLayerInfo =
-        ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc);
-
-    for (unsigned int i = 0; i != maxTime; ++i)
-    {
-
-        // Set LSTM input and output ITensors depending on:
-        // input format (timeMajor) & number of LSTM batches (maxTime).
-        arm_compute::ITensorInfo* outputLSTM;
-        arm_compute::ITensorInfo* inputLSTM;
-        // If there is only one LSTM time major batch, we will not concat OR permute.
-        // Set input of LSTM to be first input ITensor.
-        // Set output of LSTM to be final output ITensor.
-        // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
-        if (maxTime == 1 && !descriptor.m_TimeMajor)
-        {
-            TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U);
-            TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U);
-            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
-            TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
-            auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
-            auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
-            const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink);
-            inputLSTM = const_cast<arm_compute::TensorInfo*>(&aclInputInfo);
-            const_cast<arm_compute::TensorInfo*>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink);
-            outputLSTM = const_cast<arm_compute::TensorInfo*>(&aclOutputInfo);
-        }
-            // If there is only one LSTM batch major batch, we will not concat, only permute.
-            // Set input of LSTM to be output of initial permute.
-            // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
-            // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
-        else if (maxTime == 1 && !descriptor.m_TimeMajor)
-        {
-            TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U);
-            TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
-            auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
-            aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink);
-            inputLSTM = &aclPermuteOutInfo;
-            outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
-        }
-            // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
-        else
-        {
-            inputLSTM = splitterOutputsTensorInfosPtr[i];
-            outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
-        }
-
-        statusLSTM = arm_compute::CLLSTMLayer::validate(inputLSTM,
-                                                        &aclInputToForgetWeightsInfo,
-                                                        &aclInputToCellWeightsInfo,
-                                                        &aclInputToOutputWeightsInfo,
-                                                        &aclRecurrentToForgetWeightsInfo,
-                                                        &aclRecurrentToCellWeightsInfo,
-                                                        &aclRecurrentToOutputWeightsInfo,
-                                                        &aclForgetGateBiasInfo,
-                                                        &aclCellBiasInfo,
-                                                        &aclOutputGateBiasInfo,
-                                                        &aclOutputStateInInfo,
-                                                        &aclCellStateInInfo,
-                                                        &aclScratchBufferInfo,
-                                                        &aclOutputStateOutInfo,
-                                                        &aclCellStateOutInfo,
-                                                        outputLSTM,
-                                                        lstm_params_info,
-                                                        activationLayerInfo,
-                                                        cell_threshold,
-                                                        projection_threshold);
-
-        if (statusLSTM.error_code() != arm_compute::ErrorCode::OK)
-        {
-            break;
-        }
-    }
-
-    //
-    // Concat validate
-    //
-
-    // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
-    TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U);
-    TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
-    TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
-
-    TensorInfo concatOuputTensorInfo = TensorInfo(output);
-    concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
-    arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo);
-
-    if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
-    {
-        for (unsigned int i = 0; i < maxTime; ++i)
-        {
-            auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor);
-            concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand);
-        }
-
-        unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension);
-        if (!descriptor.m_TimeMajor)
-        {
-            statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,
-                                                                     &aclConcatOuputTensorInfo,
-                                                                     aclAxisConcat);
-        }
-        else
-        {
-            statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr,
-                                                                     &aclOutputInfo,
-                                                                     aclAxisConcat);
-        }
-    }
-    // If only one LSTM batch, we do not concat and/or permute.
-    // Must ensure final output info is expanded to correct batch major dimensions.
-    else
-    {
-        if (!descriptor.m_TimeMajor)
-        {
-            const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
-                BuildArmComputeTensorShape(shapeExpandBatchMajor));
-        }
-        else
-        {
-            const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
-                BuildArmComputeTensorShape(shapeExpandTimeMajor));
-        }
-    }
-    //
-    // Permute validate
-    //
-    if (!descriptor.m_TimeMajor)
-    {
-        // Output now time major. Permute output back to batch major.
-        if (maxTime != 1)
-        {
-            statusPermute2 = arm_compute::CLPermute::validate(&aclConcatOuputTensorInfo,
-                                                              &aclOutputInfo,
-                                                              arm_compute::PermutationVector(0U, 2U, 1U));
-        }
-        else
-        {
-            statusPermute2 = arm_compute::CLPermute::validate(concatInputsTensorInfosPtr[0],
-                                                              &aclOutputInfo,
-                                                              arm_compute::PermutationVector(0U, 2U, 1U));
-        }
-    }
-
-    auto okCode = arm_compute::ErrorCode::OK;
-    if (statusPermute1.error_code() == okCode &&
-        statusSplit.error_code()    == okCode &&
-        statusLSTM .error_code()    == okCode &&
-        statusConcat.error_code()   == okCode &&
-        statusPermute2.error_code() == okCode)
-    {
-        return arm_compute::Status(arm_compute::ErrorCode::OK,
-                                   "All Unidirectional Sequence LSTM layer validate status OK.");
-    }
-    else
-    {
-        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
-                                   "Unidirectional Sequence LSTM layer validate status failed.");
-    }
-}
-
-void ClUnidirectionalSequenceLstmFloatWorkload::FreeUnusedTensors()
-{
-    FreeTensorIfUnused(m_InputToInputWeightsTensor);
-    FreeTensorIfUnused(m_InputToForgetWeightsTensor);
-    FreeTensorIfUnused(m_InputToCellWeightsTensor);
-    FreeTensorIfUnused(m_InputToOutputWeightsTensor);
-    FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);
-    FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);
-    FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);
-    FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);
-    FreeTensorIfUnused(m_CellToInputWeightsTensor);
-    FreeTensorIfUnused(m_CellToForgetWeightsTensor);
-    FreeTensorIfUnused(m_CellToOutputWeightsTensor);
-    FreeTensorIfUnused(m_InputGateBiasTensor);
-    FreeTensorIfUnused(m_ForgetGateBiasTensor);
-    FreeTensorIfUnused(m_CellBiasTensor);
-    FreeTensorIfUnused(m_OutputGateBiasTensor);
-    FreeTensorIfUnused(m_ProjectionWeightsTensor);
-    FreeTensorIfUnused(m_ProjectionBiasTensor);
-    FreeTensorIfUnused(m_InputLayerNormWeightsTensor);
-    FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);
-    FreeTensorIfUnused(m_CellLayerNormWeightsTensor);
-    FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);
-    FreeTensorIfUnused(m_ScratchBuffer);
-}
-
-} //namespace armnn
diff --git a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp
deleted file mode 100644
index f50e0a90b2..0000000000
--- a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp
+++ /dev/null
@@ -1,96 +0,0 @@
-//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/Descriptors.hpp>
-#include <armnn/LstmParams.hpp>
-#include <armnn/backends/Workload.hpp>
-#include <armnn/backends/WorkloadData.hpp>
-
-#include <arm_compute/graph/Tensor.h>
-#include <arm_compute/runtime/CL/functions/CLLSTMLayer.h>
-#include <arm_compute/runtime/CL/functions/CLPermute.h>
-#include <arm_compute/runtime/CL/functions/CLSplit.h>
-#include <arm_compute/runtime/CL/functions/CLConcatenateLayer.h>
-
-namespace armnn
-{
-
-class ClUnidirectionalSequenceLstmFloatWorkload : public FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>
-{
-public:
-    ClUnidirectionalSequenceLstmFloatWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
-                                              const WorkloadInfo& info,
-                                              const arm_compute::CLCompileContext& clCompileContext);
-    virtual void Execute() const override;
-
-private:
-
-    //
-    // ACL layers required to fully form a Unidirectional Sequence LSTM layer.
-    //
-
-    // permutation for input (only used when input is batch major)
-    mutable std::unique_ptr<arm_compute::CLPermute> m_Permute1;
-    mutable std::unique_ptr<arm_compute::IFunction> m_Splitter;
-    mutable std::vector<std::unique_ptr<arm_compute::CLLSTMLayer>> m_Layers;
-    mutable std::unique_ptr<arm_compute::CLConcatenateLayer> m_Concat;
-    // permutation for output (only used when input is batch major)
-    mutable std::unique_ptr<arm_compute::CLPermute> m_Permute2;
-
-    //
-    // ACL LSTM arm_compute::CLTensors.
-    //
-    std::unique_ptr<arm_compute::CLTensor> m_InputToInputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_InputToForgetWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_InputToCellWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_InputToOutputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_RecurrentToInputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_RecurrentToForgetWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_RecurrentToCellWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_RecurrentToOutputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_CellToInputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_CellToForgetWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_CellToOutputWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_InputGateBiasTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_ForgetGateBiasTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_CellBiasTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_OutputGateBiasTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_ProjectionWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_ProjectionBiasTensor;
-
-    std::unique_ptr<arm_compute::CLTensor> m_ScratchBuffer;
-
-    std::unique_ptr<arm_compute::CLTensor> m_InputLayerNormWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_ForgetLayerNormWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_CellLayerNormWeightsTensor;
-    std::unique_ptr<arm_compute::CLTensor> m_OutputLayerNormWeightsTensor;
-
-    //
-    // Additional ACL arm_compute::CLTensors and std::vector<arm_compute::CLTensor>.
-    // Required to perform splitting, concatenation and permutations.
-    //
-    arm_compute::CLTensor m_PermuteFirstOut;
-    std::vector<arm_compute::CLTensor> m_SplitterOutputsTensors;
-    std::vector<arm_compute::CLTensor> m_ConcatInputsTensors;
-    std::vector<arm_compute::ICLTensor*> m_SplitterOutputs;
-    std::vector<const arm_compute::ICLTensor*> m_ConcatInputs;
-    arm_compute::CLTensor concat_out;
-
-    void FreeUnusedTensors();
-};
-
-arm_compute::Status
-ClUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input,
-                                                  const TensorInfo& outputStateIn,
-                                                  const TensorInfo& cellStateIn,
-                                                  const TensorInfo& output,
-                                                  const Optional<TensorInfo>& hiddenStateOutput,
-                                                  const Optional<TensorInfo>& cellStateOutput,
-                                                  const UnidirectionalSequenceLstmDescriptor& descriptor,
-                                                  const LstmInputParamsInfo& paramsInfo);
-
-} //namespace armnn
diff --git a/src/backends/cl/workloads/ClWorkloads.hpp b/src/backends/cl/workloads/ClWorkloads.hpp
index 3558da341e..bb04b17d32 100644
--- a/src/backends/cl/workloads/ClWorkloads.hpp
+++ b/src/backends/cl/workloads/ClWorkloads.hpp
@@ -65,4 +65,3 @@
 #include "ClConvertFp32ToFp16Workload.hpp"
 #include "ClTransposeConvolution2dWorkload.hpp"
 #include "ClTransposeWorkload.hpp"
-#include "ClUnidirectionalSequenceLstmFloatWorkload.hpp"
diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp
index e2098a310f..8901e47a0a 100644
--- a/src/backends/neon/NeonLayerSupport.cpp
+++ b/src/backends/neon/NeonLayerSupport.cpp
@@ -182,13 +182,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                              infos[1],
                                              *(PolymorphicDowncast<const BatchToSpaceNdDescriptor*>(&descriptor)),
                                              reasonIfUnsupported);
-        case LayerType::Cast:
-            return IsCastSupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::ChannelShuffle:
-            return IsChannelShuffleSupported(infos[0],
-                                             infos[1],
-                                             *(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)),
-                                             reasonIfUnsupported);
         case LayerType::Comparison:
             return IsComparisonSupported(infos[0],
                                          infos[1],
@@ -245,34 +238,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                                 reasonIfUnsupported);
             }
         }
-        case LayerType::Convolution3d:
-        {
-            if (infos.size() != 4)
-            {
-                throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. "
-                                               "TensorInfos should be of format: {input, output, weights, biases}.");
-            }
-
-            auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor));
-            if (infos[3] == TensorInfo())
-            {
-                return IsConvolution3dSupported(infos[0],
-                                                infos[1],
-                                                desc,
-                                                infos[2],
-                                                EmptyOptional(),
-                                                reasonIfUnsupported);
-            }
-            else
-            {
-                return IsConvolution3dSupported(infos[0],
-                                                infos[1],
-                                                desc,
-                                                infos[2],
-                                                infos[3],
-                                                reasonIfUnsupported);
-            }
-        }
         case LayerType::DepthToSpace:
             return IsDepthToSpaceSupported(infos[0],
                                            infos[1],
@@ -308,19 +273,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
         }
         case LayerType::Dequantize:
             return IsDequantizeSupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::DetectionPostProcess:
-        {
-            auto desc = *(PolymorphicDowncast<const DetectionPostProcessDescriptor*>(&descriptor));
-            return LayerSupportBase::IsDetectionPostProcessSupported(infos[0],
-                                                                     infos[1],
-                                                                     infos[2],
-                                                                     infos[3],
-                                                                     infos[4],
-                                                                     infos[5],
-                                                                     infos[6],
-                                                                     desc,
-                                                                     reasonIfUnsupported);
-        }
         case LayerType::Division:
             return IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
         case LayerType::ElementwiseUnary:
@@ -383,8 +335,27 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                    *(PolymorphicDowncast<const LstmDescriptor*>(&descriptor)),
                                    lstmParamsInfo.value(),
                                    reasonIfUnsupported);
-        case LayerType::Map:
-            return true;
+        case LayerType::QLstm:
+            return IsQLstmSupported(infos[0],
+                                    infos[1],
+                                    infos[2],
+                                    infos[3],
+                                    infos[4],
+                                    infos[5],
+                                    *(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)),
+                                    lstmParamsInfo.value(),
+                                    reasonIfUnsupported);
+        case LayerType::UnidirectionalSequenceLstm:
+            return IsUnidirectionalSequenceLstmSupported(infos[0],
+                                                         infos[1],
+                                                         infos[2],
+                                                         infos[3],
+                                                         infos[4],
+                                                         infos[5],
+                                                         *(PolymorphicDowncast<const
+                                                            UnidirectionalSequenceLstmDescriptor*>(&descriptor)),
+                                                         lstmParamsInfo.value(),
+                                                         reasonIfUnsupported);
         case LayerType::Maximum:
             return IsMaximumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
         case LayerType::Mean:
@@ -392,15 +363,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                    infos[1],
                                    *(PolymorphicDowncast<const MeanDescriptor*>(&descriptor)),
                                    reasonIfUnsupported);
-        case LayerType::MemCopy:
-            return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::MemImport:
-            return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported);
-        case LayerType::Merge:
-            return LayerSupportBase::IsMergeSupported(infos[0],
-                                                      infos[1],
-                                                      infos[2],
-                                                      reasonIfUnsupported);
         case LayerType::Minimum:
             return IsMinimumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
         case LayerType::Multiplication:
@@ -429,16 +391,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                         reasonIfUnsupported);
         case LayerType::Prelu:
             return IsPreluSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
-        case LayerType::QLstm:
-            return IsQLstmSupported(infos[0],
-                                    infos[1],
-                                    infos[2],
-                                    infos[3],
-                                    infos[4],
-                                    infos[5],
-                                    *(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)),
-                                    lstmParamsInfo.value(),
-                                    reasonIfUnsupported);
         case LayerType::Quantize:
             return IsQuantizeSupported(infos[0], infos[1], reasonIfUnsupported);
         case LayerType::QuantizedLstm:
@@ -449,8 +401,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                             infos[4],
                                             quantizedLstmParamsInfo.value(),
                                             reasonIfUnsupported);
-        case LayerType::Rank:
-            return true;
         case LayerType::Reshape:
             return IsReshapeSupported(infos[0],
                                       infos[1],
@@ -466,10 +416,6 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                      infos[1],
                                      *(PolymorphicDowncast<const ReduceDescriptor*>(&descriptor)),
                                      reasonIfUnsupported);
-        case LayerType::Shape:
-            return LayerSupportBase::IsShapeSupported(infos[0],
-                                                      infos[1],
-                                                      reasonIfUnsupported);
         case LayerType::Slice:
             return IsSliceSupported(infos[0],
                                     infos[1],
@@ -554,23 +500,77 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type,
                                                          reasonIfUnsupported);
             }
         }
-        case LayerType::UnidirectionalSequenceLstm:
-            return IsUnidirectionalSequenceLstmSupported(infos[0],
-                                                         infos[1],
-                                                         infos[2],
-                                                         infos[3],
-                                                         infos[4],
-                                                         infos[5],
-                                                         *(PolymorphicDowncast<const
-                                                            UnidirectionalSequenceLstmDescriptor*>(&descriptor)),
-                                                         lstmParamsInfo.value(),
-                                                         reasonIfUnsupported);
+        case LayerType::Cast:
+            return IsCastSupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::ChannelShuffle:
+            return IsChannelShuffleSupported(infos[0],
+                                             infos[1],
+                                             *(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)),
+                                             reasonIfUnsupported);
+        case LayerType::Convolution3d:
+        {
+            if (infos.size() != 4)
+            {
+                throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. "
+                                               "TensorInfos should be of format: {input, output, weights, biases}.");
+            }
+
+            auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor));
+            if (infos[3] == TensorInfo())
+            {
+                return IsConvolution3dSupported(infos[0],
+                                                infos[1],
+                                                desc,
+                                                infos[2],
+                                                EmptyOptional(),
+                                                reasonIfUnsupported);
+            }
+            else
+            {
+                return IsConvolution3dSupported(infos[0],
+                                                infos[1],
+                                                desc,
+                                                infos[2],
+                                                infos[3],
+                                                reasonIfUnsupported);
+            }
+        }
+        case LayerType::MemCopy:
+            return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::MemImport:
+            return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported);
+        case LayerType::DetectionPostProcess:
+        {
+            auto desc = *(PolymorphicDowncast<const DetectionPostProcessDescriptor*>(&descriptor));
+            return LayerSupportBase::IsDetectionPostProcessSupported(infos[0],
+                                                                     infos[1],
+                                                                     infos[2],
+                                                                     infos[3],
+                                                                     infos[4],
+                                                                     infos[5],
+                                                                     infos[6],
+                                                                     desc,
+                                                                     reasonIfUnsupported);
+        }
+        case LayerType::Map:
+            return true;
         case LayerType::Unmap:
             return true;
+        case LayerType::Merge:
+            return LayerSupportBase::IsMergeSupported(infos[0],
+                                                      infos[1],
+                                                      infos[2],
+                                                      reasonIfUnsupported);
+        case LayerType::Rank:
+            return true;
+        case LayerType::Shape:
+            return LayerSupportBase::IsShapeSupported(infos[0],
+                                                      infos[1],
+                                                      reasonIfUnsupported);
         default:
             // layers not supported in neon by default:
-            // debug, fakequantization, precompiled,
-            // standin, switch, pooling3d
+            // debug, fakequantization, precompiled, standin,
+            // switch, unidirectionalsequencelstm, pooling3d
             return false;
     }
 }
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
index 776afd3965..10c2ecbd19 100644
--- a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
+++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
@@ -31,13 +31,10 @@ private:
     //
     // ACL layers required to fully form a Unidirectional Sequence LSTM layer.
     //
-
-    // permutation for input (only used when input is batch major)
     mutable std::unique_ptr<arm_compute::NEPermute> m_Permute1;
     mutable std::unique_ptr<arm_compute::IFunction> m_Splitter;
     mutable std::vector<std::unique_ptr<arm_compute::NELSTMLayer>> m_Layers;
     mutable std::unique_ptr<arm_compute::NEConcatenateLayer> m_Concat;
-    // permutation for output (only used when input is batch major)
     mutable std::unique_ptr<arm_compute::NEPermute> m_Permute2;
 
     //